Rationale generation management

ABSTRACT

Disclosed aspects relate to generating rationales for treatment options. A set of preference scores that indicates a first preference score for a first treatment option of a set of treatment options may be received. A rank-order that indicates a first ranking for the first treatment option may be received. The set of preference scores may be analyzed with respect to the rank-order to determine a relationship between the first preference score and the first ranking for the first treatment option. Based on the relationship between the first preference score and the first ranking, a set of rationale data for the first treatment option may be generated with respect to the first rank. Based on a user profile for a user, the set of rationale data may be configured for the user. The set of rationale data which is configured for the user may be provided.

BACKGROUND

This disclosure relates generally to computer systems and, moreparticularly, relates to rationale generation management. The amount ofdata that needs to be managed is increasing. Management of data may bedesired to be performed as efficiently as possible. As data needing tobe managed increases, the need for rationale generation management mayalso increase.

SUMMARY

Aspects of the disclosure relate to generating rationale data for a setof treatment options. Treatment options for a user may be ranked basedon a set of attributes, and a set of rationale data may be dynamicallyprovided for each treatment option of the set of treatment options. Theset of rationale data may be configured for a user based on a userprofile. The set of rationale data may include an indication of why atreatment option was ranked where it was, as well as a candidate rankthat indicates where the treatment option would be ranked absent one ormore exclusion factors. The set of rationale data may take into accountthe factors used to determine the rank for a treatment option as well asthe factors used to determine the rank for other treatment options ofthe set of treatment options. For instance, if a particular treatmentoption is scored as the top-ranked answer of the set of treatmentoptions, the set of rationale data may indicate the reasons why it ispreferred over other treatment options. In embodiments, generating theset of rationale data may include summarizing the attributes that leadto a particular ranking for a treatment option, summarizing theattributes as well as exclusion factors that lowered the ranking of atreatment option, or summarizing the attributes and rationale forpreferred treatment exclusions.

Disclosed aspects relate to generating rationales for treatment options.A set of preference scores that indicates a first preference score for afirst treatment option of a set of treatment options may be received. Arank-order that indicates a first ranking for the first treatment optionmay be received. The set of preference scores may be analyzed withrespect to the rank-order to determine a relationship between the firstpreference score and the first ranking for the first treatment option.Based on the relationship between the first preference score and thefirst ranking, a set of rationale data for the first treatment optionmay be generated with respect to the first rank. Based on a user profilefor a user, the set of rationale data may be configured for the user.The set of rationale data which is configured for the user may beprovided.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 is a diagrammatic illustration of an example computingenvironment, according to embodiments.

FIG. 2 is a system diagram depicting a high level logical architecturefor a question answering system, according to embodiments.

FIG. 3 is a block diagram illustrating a question answering system togenerate answers to one or more input questions, according toembodiments.

FIG. 4 is a flowchart illustrating a method for generating rationalesfor a set of treatment options, according to embodiments.

FIG. 5 is a flowchart illustrating a system for generating rationalesfor a set of treatment options, according to embodiments.

FIG. 6 depicts an example of generating rationales for a set oftreatment options, according to embodiments.

FIG. 7 depicts an example of generating rationales for a set oftreatment options, according to embodiments.

FIG. 8 depicts an example of generating rationales for a set oftreatment options, according to embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure relate to generating rationale data for a setof treatment options. Treatment options for a user (e.g., patient) maybe ranked based on a set of attributes, and a set of rationale data maybe dynamically provided for each treatment option of the set oftreatment options. The set of rationale data may be configured (e.g.,customized) for a user based on a user profile. The set of rationaledata may include an indication of why a treatment option was rankedwhere it was, as well as a candidate rank that indicates where thetreatment option would be ranked absent one or more exclusion factors.The set of rationale data may take into account the factors used todetermine the rank for a treatment option as well as the factors used todetermine the rank for other treatment options of the set of treatmentoptions. For instance, if a particular treatment options is scored asthe top-ranked answer of the set of treatment options, the set ofrationale data may indicate the reasons why it is preferred over othertreatment options. In embodiments, generating the set of rationale datamay include summarizing the attributes that lead to a particular rankingfor a treatment option, summarizing the attributes as well as exclusionfactors that lowered the ranking of a treatment option, or summarizingthe attributes and rationale for preferred treatment exclusions (e.g.,why other treatment operations were preferred with respect to aparticular treatment option). Leveraging rationale data with respect toa set of treatment options may be associated with benefits such astreatment option ranking validity, accuracy, and rationale generationefficiency.

Question answering systems are one tool that may be used to provide andrank treatment options for patients. Aspects of the disclosure relate tothe recognition that, in some situations, question answering systems maybe lacking with respect to provision of evidence-based rationalesexplaining how and why certain treatment options are ranked as they are.Accordingly, aspects of the disclosure relate to automatic generation ofrationale data for a set of treatment options. A rank-order assigned toa set of treatment options may be evaluated with respect to a set ofpreference scores for the set of treatment options to indicate thedegree to which the set of preference scores match the rank-order. Basedon a relationship between the set of preference scores and therank-order, a set of rationale data that indicates the attributes andfactors that led to the ranking of a particular treatment option may bedynamically generated. As such, dynamic rationale generation may promoteautomated treatment option reliability and facilitate treatment optionselection.

Aspects of the disclosure relate to a system, method, and computerprogram product for generating rationales for treatment options. A setof preference scores that indicates a first preference score for a firsttreatment option of a set of treatment options may be received. Arank-order that indicates a first ranking for the first treatment optionmay be received. The set of preference scores may be analyzed withrespect to the rank-order to determine a relationship between the firstpreference score and the first ranking for the first treatment option.Based on the relationship between the first preference score and thefirst ranking, a set of rationale data for the first treatment optionmay be generated with respect to the first rank. Based on a user profilefor a user, the set of rationale data may be configured for the user.The set of rationale data which is configured for the user may beprovided.

In embodiments a set of attributes that characterize a merit feature ofthe set of treatment options may be detected to determine therelationship between the first preference score and the first rankingfor the first treatment option. In embodiments a set of exclusionfactors that characterize a demerit feature of the set of treatmentoptions may be detected to determine the relationship between the firstpreference score and the first ranking for the first treatment option.In embodiments, it may be determined that the first preference scoresachieves a match with respect to the first ranking based on therelationship between the first preference score and the first ranking,and a first subset of attributes of the set of attributes thatcorresponds to the first treatment option of the set of treatmentoptions may be compiled to generate the set of rationale data in anautomated fashion. In embodiments, it may be determined that the firstpreference score fails to achieve a match with respect to the firstranking based on the relationship between the first preference score andthe first ranking, and both a first subset of attributes of the set ofattributes that corresponds to the first treatment option of the set oftreatment options and a first subset of exclusion factors of the set ofexclusions factors that corresponds to the first treatment option of theset of treatment options may be compiled to generate the set ofrationale data in an automated fashion. In embodiments, it may bedetermined that the first preference score achieves a discrepancythreshold with respect to the first ranking based on the relationshipbetween the first preference score and the first ranking, and both asecond subset of attributes of the set of attributes that corresponds toa second treatment option of the set of treatment options and a secondsubset of exclusion factors of the set of exclusion factors thatcorresponds to the second treatment option of the set of treatmentoptions may be compiled to generate the set of rationale data in anautomated fashion. In embodiments, the set of attributes may be filteredin an automated fashion based on a set of attribute priority criteria toremove a second subset of attributes from the set of attributes.Altogether, performance or efficiency benefits with respect to rationalegeneration management may occur. Aspects may save resources such asbandwidth, processing, or memory.

Turning now to the figures, FIG. 1 is a diagrammatic illustration of anexemplary computing environment, consistent with embodiments of thepresent disclosure. In certain embodiments, the environment 100 caninclude one or more remote devices 102, 112 and one or more host devices122. Remote devices 102, 112 and host device 122 may be distant fromeach other and communicate over a network 150 in which the host device122 comprises a central hub from which remote devices 102, 112 canestablish a communication connection. Alternatively, the host device andremote devices may be configured in any other suitable relationship(e.g., in a peer-to-peer or other relationship).

In certain embodiments the network 100 can be implemented by any numberof any suitable communications media (e.g., wide area network (WAN),local area network (LAN), Internet, Intranet, etc.). Alternatively,remote devices 102, 112 and host devices 122 may be local to each other,and communicate via any appropriate local communication medium (e.g.,local area network (LAN), hardwire, wireless link, Intranet, etc.). Incertain embodiments, the network 100 can be implemented within a cloudcomputing environment, or using one or more cloud computing services.Consistent with various embodiments, a cloud computing environment caninclude a network-based, distributed data processing system thatprovides one or more cloud computing services. In certain embodiments, acloud computing environment can include many computers, hundreds orthousands of them, disposed within one or more data centers andconfigured to share resources over the network.

In certain embodiments, host device 122 can include a question answeringsystem 130 (also referred to herein as a QA system) having a searchapplication 134 and an answer module 132. In certain embodiments, thesearch application may be implemented by a conventional or other searchengine, and may be distributed across multiple computer systems. Thesearch application 134 can be configured to search one or more databasesor other computer systems for content that is related to a questioninput by a user at a remote device 102, 112.

In certain embodiments, remote devices 102, 112 enable users to submitquestions (e.g., search requests or other queries) to host devices 122to retrieve search results. For example, the remote devices 102, 112 mayinclude a query module 120 (e.g., in the form of a web browser or anyother suitable software module) and present a graphical user (e.g., GUI,etc.) or other interface (e.g., command line prompts, menu screens,etc.) to solicit queries from users for submission to one or more hostdevices 122 and further to display answers/results obtained from thehost devices 122 in relation to such queries.

Consistent with various embodiments, host device 122 and remote devices102, 112 may be computer systems preferably equipped with a display ormonitor. In certain embodiments, the computer systems may include atleast one processor 106, 116, 126 memories 108, 118, 128 and/or internalor external network interface or communications devices 104, 114, 124(e.g., modem, network cards, etc.), optional input devices (e.g., akeyboard, mouse, or other input device), and any commercially availableand custom software (e.g., browser software, communications software,server software, natural language processing software, search engineand/or web crawling software, filter modules for filtering content basedupon predefined criteria, etc.). In certain embodiments, the computersystems may include server, desktop, laptop, and hand-held devices. Inaddition, the answer module 132 may include one or more modules or unitsto perform the various functions of present disclosure embodimentsdescribed below (e.g., receiving an input question, evaluating thequality of the input question, assigning a set of quality values, andgenerating an icon), and may be implemented by any combination of anyquantity of software and/or hardware modules or units.

FIG. 2 is a system diagram depicting a high-level logical architecture200 for a question answering system (also referred to herein as a QAsystem), consistent with embodiments of the present disclosure. Aspectsof FIG. 2 are directed toward components for use with a QA system. Incertain embodiments, the question analysis component 204 can receive anatural language question from a remote device 202, and can analyze thequestion to produce, minimally, the semantic type of the expectedanswer. The search component 206 can formulate queries from the outputof the question analysis component 204 and may consult various resourcessuch as the internet or one or more knowledge resources, e.g.,databases, corpora 208, to retrieve documents, passages, web-pages,database tuples, etc., that are relevant to answering the question. Forexample, as shown in FIG. 2, in certain embodiments, the searchcomponent 206 can consult a corpus of information 208 on a host device225. The candidate answer generation component 210 can then extract fromthe search results potential (candidate) answers to the question, whichcan then be scored and ranked by the answer selection component 212which may produce a final ranked list of answers with associatedconfidence measure values.

The various components of the exemplary high level logical architecturefor a QA system described above may be used to implement various aspectsof the present disclosure. For example, the question analysis component204 could, in certain embodiments, be used to process a natural languagequestion for which relevant images can be provided. Further, the searchcomponent 206 can, in certain embodiments, be used to perform a searchof a corpus of information 208 for a set of images that are related toan answer to an input question to the QA system. The candidategeneration component 210 can be used to identify a set of candidateimages based on the results of the search component 206. Further, theanswer selection component 212 can, in certain embodiments, be used todetermine and select a subset of the set of candidate images to providein a display area. In certain embodiments, the determination of thesubset of the candidate images can be based on a confidence value of theset of images and a designated display specification.

FIG. 3 is a block diagram illustrating a question answering system (alsoreferred to herein as a QA system) to generate answers to one or moreinput questions, consistent with various embodiments of the presentdisclosure. Aspects of FIG. 3 are directed toward an exemplary systemarchitecture 300 of a question answering system 312 to generate answersto queries (e.g., input questions). In certain embodiments, one or moreusers may send requests for information to QA system 312 using a remotedevice (such as remote devices 102, 112 of FIG. 1). QA system 312 canperform methods and techniques for responding to the requests sent byone or more client applications 308. Client applications 308 may involveone or more entities operable to generate events dispatched to QA system312 via network 315. In certain embodiments, the events received at QAsystem 312 may correspond to input questions received from users, wherethe input questions may be expressed in a free form and in naturallanguage.

A question (similarly referred to herein as a query) may be one or morewords that form a search term or request for data, information orknowledge. A question may be expressed in the form of one or morekeywords. Questions may include various selection criteria and searchterms. A question may be composed of complex linguistic features, notonly keywords. However, keyword-based search for answer is alsopossible. In certain embodiments, using unrestricted syntax forquestions posed by users is enabled. The use of restricted syntaxresults in a variety of alternative expressions for users to betterstate their needs.

Consistent with various embodiments, client applications 308 can includeone or more components such as a search application 302 and a mobileclient 310. Client applications 308 can operate on a variety of devices.Such devices include, but are not limited to, mobile and handhelddevices, such as laptops, mobile phones, personal or enterprise digitalassistants, and the like; personal computers, servers, or other computersystems that access the services and functionality provided by QA system312. For example, mobile client 310 may be an application installed on amobile or other handheld device. In certain embodiments, mobile client310 may dispatch query requests to QA system 312.

Consistent with various embodiments, search application 302 can dispatchrequests for information to QA system 312. In certain embodiments,search application 302 can be a client application to QA system 312. Incertain embodiments, search application 302 can send requests foranswers to QA system 312. Search application 302 may be installed on apersonal computer, a server or other computer system. In certainembodiments, search application 302 can include a search graphical userinterface (GUI) 304 and session manager 306. Users may enter questionsin search GUI 304. In certain embodiments, search GUI 304 may be asearch box or other GUI component, the content of which represents aquestion to be submitted to QA system 312. Users may authenticate to QAsystem 312 via session manager 306. In certain embodiments, sessionmanager 306 keeps track of user activity across sessions of interactionwith the QA system 312. Session manager 306 may keep track of whatquestions are submitted within the lifecycle of a session of a user. Forexample, session manager 306 may retain a succession of questions posedby a user during a session. In certain embodiments, answers produced byQA system 312 in response to questions posed throughout the course of auser session may also be retained. Information for sessions managed bysession manager 306 may be shared between computer systems and devices.

In certain embodiments, client applications 308 and QA system 312 can becommunicatively coupled through network 315, e.g. the Internet,intranet, or other public or private computer network. In certainembodiments, QA system 312 and client applications 308 may communicateby using Hypertext Transfer Protocol (HTTP) or Representational StateTransfer (REST) calls. In certain embodiments, QA system 312 may resideon a server node. Client applications 308 may establish server-clientcommunication with QA system 312 or vice versa. In certain embodiments,the network 315 can be implemented within a cloud computing environment,or using one or more cloud computing services. Consistent with variousembodiments, a cloud computing environment can include a network-based,distributed data processing system that provides one or more cloudcomputing services.

Consistent with various embodiments, QA system 312 may respond to therequests for information sent by client applications 308, e.g., posedquestions by users. QA system 312 can generate answers to the receivedquestions. In certain embodiments, QA system 312 may include a questionanalyzer 314, data sources 324, and answer generator 328. Questionanalyzer 314 can be a computer module that analyzes the receivedquestions. In certain embodiments, question analyzer 314 can performvarious methods and techniques for analyzing the questions syntacticallyand semantically. In certain embodiments, question analyzer 314 canparse received questions. Question analyzer 314 may include variousmodules to perform analyses of received questions. For example, computermodules that question analyzer 314 may include, but are not limited to atokenizer 316, part-of-speech (POS) tagger 318, semantic relationshipidentification 320, and syntactic relationship identification 322.

Consistent with various embodiments, tokenizer 316 may be a computermodule that performs lexical analysis. Tokenizer 316 can convert asequence of characters into a sequence of tokens. Tokens may be stringof characters typed by a user and categorized as a meaningful symbol.Further, in certain embodiments, tokenizer 316 can identify wordboundaries in an input question and break the question or any text intoits component parts such as words, multiword tokens, numbers, andpunctuation marks. In certain embodiments, tokenizer 316 can receive astring of characters, identify the lexemes in the string, and categorizethem into tokens.

Consistent with various embodiments, POS (part of speech) tagger 318 canbe a computer module that marks up a word in a text to correspond to aparticular part of speech. POS tagger 318 can read a question or othertext in natural language and assign a part of speech to each word orother token. POS tagger 318 can determine the part of speech to which aword corresponds based on the definition of the word and the context ofthe word. The context of a word may be based on its relationship withadjacent and related words in a phrase, sentence, question, orparagraph. In certain embodiments, context of a word may be dependent onone or more previously posed questions. Examples of parts of speech thatmay be assigned to words include, but are not limited to, nouns, verbs,adjectives, adverbs, and the like. Examples of other part of speechcategories that POS tagger 318 may assign include, but are not limitedto, comparative or superlative adverbs, wh-adverbs, conjunctions,determiners, negative particles, possessive markers, prepositions,wh-pronouns, and the like. In certain embodiments, POS tagger 316 cantag or otherwise annotates tokens of a question with part of speechcategories. In certain embodiments, POS tagger 316 can tag tokens orwords of a question to be parsed by QA system 312.

Consistent with various embodiments, semantic relationshipidentification 320 may be a computer module that can identify semanticrelationships of recognized entities in questions posed by users. Incertain embodiments, semantic relationship identification 320 maydetermine functional dependencies between entities, the dimensionassociated to a member, and other semantic relationships.

Consistent with various embodiments, syntactic relationshipidentification 322 may be a computer module that can identify syntacticrelationships in a question composed of tokens posed by users to QAsystem 312. Syntactic relationship identification 322 can determine thegrammatical structure of sentences, for example, which groups of wordsare associated as “phrases” and which word is the subject or object of averb. In certain embodiments, syntactic relationship identification 322can conform to a formal grammar.

In certain embodiments, question analyzer 314 may be a computer modulethat can parse a received query and generate a corresponding datastructure of the query. For example, in response to receiving a questionat QA system 312, question analyzer 314 can output the parsed questionas a data structure. In certain embodiments, the parsed question may berepresented in the form of a parse tree or other graph structure. Togenerate the parsed question, question analyzer 130 may trigger computermodules 132-144. Question analyzer 130 can use functionality provided bycomputer modules 316-322 individually or in combination. Additionally,in certain embodiments, question analyzer 130 may use external computersystems for dedicated tasks that are part of the question parsingprocess.

Consistent with various embodiments, the output of question analyzer 314can be used by QA system 312 to perform a search of one or more datasources 324 to retrieve information to answer a question posed by auser. In certain embodiments, data sources 324 may include datawarehouses, information corpora, data models, and document repositories.In certain embodiments, the data source 324 can be an information corpus326. The information corpus 326 can enable data storage and retrieval.In certain embodiments, the information corpus 326 may be a storagemechanism that houses a standardized, consistent, clean and integratedform of data. The data may be sourced from various operational systems.Data stored in the information corpus 326 may be structured in a way tospecifically address reporting and analytic requirements. In oneembodiment, the information corpus may be a relational database (e.g.,conform to an ontology). In some example embodiments, data sources 324may include one or more document repositories.

In certain embodiments, answer generator 328 may be a computer modulethat generates answers to posed questions. Examples of answers generatedby answer generator 328 may include, but are not limited to, answers inthe form of natural language sentences; reports, charts, or otheranalytic representation; raw data; web pages, and the like.

Consistent with various embodiments, answer generator 328 may includequery processor 330, visualization processor 332 and feedback handler334. When information in a data source 324 matching a parsed question islocated, a technical query associated with the pattern can be executedby query processor 330. Based on retrieved data by a technical queryexecuted by query processor 330, visualization processor 332 can rendervisualization of the retrieved data, where the visualization representsthe answer. In certain embodiments, visualization processor 332 mayrender various analytics to represent the answer including, but notlimited to, images, charts, tables, dashboards, maps, and the like. Incertain embodiments, visualization processor 332 can present the answerto the user in understandable form.

In certain embodiments, feedback handler 334 can be a computer modulethat processes feedback from users on answers generated by answergenerator 328. In certain embodiments, users may be engaged in dialogwith the QA system 312 to evaluate the relevance of received answers.Answer generator 328 may produce a list of answers corresponding to aquestion submitted by a user. The user may rank each answer according toits relevance to the question. In certain embodiments, the feedback ofusers on generated answers may be used for future question answeringsessions.

The various components of the exemplary question answering systemdescribed above may be used to implement various aspects of the presentdisclosure. For example, the client application 308 could be used toreceive an input question having a set of query attributes. The questionanalyzer 314 could, in certain embodiments, be used to determine arelationship between a first preference score and a first ranking for atreatment option by analyzing a set of preference scores with respect toa rank-order. Further, the question answering system 312 could, incertain embodiments, be used to perform a search of an informationcorpus 326 for data that may be used to generate a set of rationale datafor the first treatment option with respect to the first rank. Theanswer generator 328 can be used generate the set of rationale data forthe first treatment option with respect to the first rank, and configurethe set of rationale data for a user based on a user profile. Further,the visualization processor 332 can, in certain embodiments, be used toprovide the set of rationale data which is configured for the user in adesignated display area.

FIG. 4 is a flowchart illustrating a method 400 for generatingrationales for a set of treatment options, according to embodiments.Question answering systems are one tool that may be used to provide andrank treatment options for patients. Aspects of the disclosure relate tothe recognition that, in some situations, question answering systems maybe lacking with respect to provision of evidence-based rationalesexplaining how and why certain treatment options are ranked as they are.Accordingly, aspects of the disclosure relate to automatic generation ofrationale data for a set of treatment options. A rank-order assigned toa set of treatment options may be evaluated with respect to a set ofpreference scores for the set of treatment options to indicate thedegree to which the set of preference scores match the rank-order. Basedon a relationship between the set of preference scores and therank-order, a set of rationale data that indicates the attributes andfactors that led to the ranking of a particular treatment option may bedynamically generated. Accordingly, aspects of the method 400 relate togenerating a set of rationale data for a first treatment option withrespect to a first ranking based on a relationship between a firstpreference score and a the first ranking for the first treatment option.Generally, the set of treatment options may include a collection ofcandidate choices regarding care solutions for a user (e.g., patient).For instance, the set of treatment options may include medical careoptions such as medication prescriptions, therapies, surgeries,prescriptions, regimens (e.g., diet, exercise) operations, or the like.As an example, the set of treatment options may include a list ofcandidate oncology treatments such as radiation therapy, chemotherapy,stem cell transplants, hormone therapy, immunotherapy, and the like.Other types of treatment options are also possible. The method 400 maybegin at block 401.

At block 410, a set of preference scores may be received with respect tothe set of treatment options. The set of preference scores may indicatea first preference score for a first treatment option of the set oftreatment options. Generally, receiving can include sensing, acquiring,detecting, collecting, obtaining, capturing, ingesting, or otherwiseaccepting delivery of the set of preference scores with respect to theset of treatment options. The set of preference scores may include acollection of values, grades, weighting factors, ratings, or otherparameters that provide a quantitative indication of the appropriatenessor suitability of one or more treatment options of the set of treatmentoptions for a user. In embodiments, the set of preference scores mayindicate the suitability of a treatment option for a user based on theassumption that the user is not associated with other attributes orfactors (e.g., comorbidities) that would affect the treatment (e.g., anideal patient). In certain embodiments, the set of preference scores maybe expressed as integer values between 0 and 10, where lower valuesindicate lower preference for a treatment option and greater valuesindicate greater preference for a treatment option (e.g., preferencescores of 1-3 may indicate a treatment option is not recommended,preference scores of 4-6 may indicate that a treatment option is forconsideration, and preference scores of 7-10 may indicate that atreatment option is recommended). As described herein, the set ofpreference scores may include a first preference score for a firsttreatment option. The first preference score may include a particularpreference score assigned to a certain treatment option of the set oftreatment options. As an example, the first preference score mayindicate a value of 2 for a treatment option of dose-densedoxorubicin/cyclophosphamide followed by paclitaxel. In embodiments,receiving the set of preference scores may include collecting the set ofpreference scores from one or more users (e.g., doctors). In certainembodiments, receiving the set of preference scores may include using atreatment option management engine to compute the set of preferencescores for the set of treatment options based on a historical archive ofmedical records (e.g., clinical trial results, past usages, medicaljournals, doctors opinions). Other methods of receiving the set ofpreference scores with respect to the set of treatment options are alsopossible.

At block 420, a rank-order may be received with respect to the set oftreatment options. The rank-order may indicate a first ranking for thefirst treatment option of the set of treatment options. Generally,receiving can include sensing, acquiring, detecting, collecting,obtaining, capturing, ingesting, or otherwise accepting delivery of therank-order that indicates the first ranking for the first treatmentoption of the set of treatment options. The rank-order may include agrouping, arrangement, classification, or organization for the set oftreatment options that designates a position for a particular treatmentoption relative to other treatment options of the set of treatmentoptions. In embodiments, the rank-order may define a hierarchy for theset of treatment options, such that those treatment options locatedhigher within the rank-order are generally associated with greaterreliability, suitability, or preference (e.g., with respect to aparticular user). As an example, for a set of treatment options ofbrachytherapy, proton therapy, stereotactic body radiation therapy, andtomotherapy, a rank-order may be received that defines a hierarchy oftomotherapy (e.g., rank 1) followed by proton therapy (e.g., rank 2),brachytherapy (e.g., rank 3), and stereotactic body radiation therapy(e.g., rank 4). As described herein, the rank-order may indicate a firstranking for a first treatment option of the set of treatment options.The first ranking may include a particular ranking (e.g., hierarchicalposition, rating) for a certain treatment option relative to othertreatment options of the set of treatment options. For instance, therank-order may indicate a first ranking of “2” for a treatment option ofintensity-modulated radiation therapy (e.g., the treatment option isranked second among the list of candidate treatment options). Inembodiments, receiving the rank-order may include using a treatmentoption management engine (e.g., question-answering system) to aggregatea plurality of scores (e.g., the set of preference scores together witha number of other scores), user attributes (e.g., characteristics thatdescribe the condition of a patient), and other factors (e.g., treatmentcompatibilities, relationship between a treatment option and one or moreuser attributes) to generate the rank-order for the set of treatmentoptions. Other methods of receiving the rank-order indicating the firstranking for the first treatment option of the set of treatment optionswith respect to the set of treatment options are also possible.

At block 430, a relationship between the first preference score and thefirst ranking for the first treatment option may be determined. Therelationship may be determined by analyzing the set of preference scoreswith respect to the rank-order. Generally, determining can includecomputing, formulating, detecting, extracting, calculating, identifying,or otherwise ascertaining the relationship between the first preferencescore and the first ranking for the first treatment option. Therelationship may include an association, correspondence, interrelation,or correlation between the first preference score and the first rankingfor the first treatment option. In embodiments, the relationship mayindicate a degree of similarity or disparity between the firstpreference score and the first ranking for the first treatment option.For instance, the relationship may indicate whether the first preferencescore substantially matches the first ranking (e.g., the firstpreference score and the first ranking rate the first treatment optionsimilarly) or whether the first preference score substantiallymismatches the first ranking (e.g., the first preference score and thefirst ranking rate the first treatment option differently). Inembodiments, determining the relationship may include analyzing (e.g.,examining, appraising, assessing, evaluating) the first preference scorewith respect to the first ranking to compute a correlation score (e.g.,indication of the degree/extent of similarity), and subsequentlycomparing the correlation score for the first preference score and thefirst ranking to a designated similarity threshold. As such, preferencescores and rankings having correlation scores that achieve thedesignated similarity threshold may be ascertained to constitute amatch, while preference scores and rankings having correlation scoresthat fail to achieve the designated similarity threshold may beascertained to constitute a mismatch. Consider the following example. Aset of preference scores may include a first preference score for afirst treatment option of “9” (e.g., highly recommended), and arank-order may indicate a first ranking for the first treatment optionof “3rd among 50 treatment options.” The first preference score and thefirst ranking may be compared, and a correlation score of “96%” (e.g.,the degree of similarity between the first preference score and thefirst treatment option) may be computed and compared to a designatedsimilarity threshold of “90%.” Accordingly, a relationship thatindicates a substantial match between the first preference score and thefirst ranking may be determined. Other methods of determining therelationship between the first preference score and the first rankingfor the first treatment option by analyzing the set of preference scoreswith respect to the rank-order are also possible.

In embodiments, a set of attributes may be detected to determine therelationship between the first preference score and the first rankingfor the first treatment option at block 431. The set of attributes maycharacterize a merit feature of the set of treatment options. Generally,detecting can include sensing, discovering, computing, calculating,distinguishing, ascertaining, or otherwise determining the set ofattributes to determine the relationship between the first preferencescore and the first ranking for the first treatment option. The set ofattributes may include a collection of traits, properties, qualities,characteristics, or features of the set of treatment options thatcontribute to determination of the rank-order for the set of treatmentoptions. In embodiments, the set of attributes may characterize a meritfeature of the set of treatment options. The merit feature may include abenefit, advantage, or positive impact associated with the set oftreatment options (e.g., that positively impacted the ranking of atreatment option in the rank-order). For instance, the set of attributesmay include reasons that the set of treatment options were chosen for aparticular user, the types of conditions on which the set of treatmentoptions are effective, or the qualities of the set of treatment optionsthat make them suitable for use. As examples, the set of attributes mayinclude a list of properties such as “effective on tumors less than 4 cmin diameter,” “positive impact on stages of N2 or less” or the like. Inembodiments, detecting the set of attributes may include parsing atreatment option management database to identify and extract the set ofattributes used to determine the rank-order for the set of treatmentoptions. Other methods of detecting the set of attributes to determinethe relationship between the first preference score and the firstranking for the first treatment option are also possible.

In embodiments, a set of exclusion factors may be detected to determinethe relationship between the first preference score and the firstranking for the first treatment option at block 432. The set ofexclusion factors may characterize a demerit feature of the set oftreatment options. Generally, detecting can include sensing,discovering, computing, calculating, distinguishing, ascertaining, orotherwise determining the set of exclusion factors to determine therelationship between the first preference score and the first rankingfor the first treatment option. The set of exclusion factors may includea collection of traits, properties, qualities, characteristics, orfeatures of the set of set of treatment options that contribute todetermination of the rank-order for the set of treatment options. Inembodiments, the set of exclusion factors that characterize a demeritfeature of the set of treatment options may include a detriment,drawback, disadvantage, or negative impact associated with the set oftreatment options (e.g., that negatively impacted the ranking of one ormore treatment options in the rank-order). For instance, the set ofexclusion factors may include reasons that particular treatment optionsof the set of treatment options were not chosen for a particular user,why a particular treatment option was not rated more highly in therank-order, context information that impacted the rank-order, or thelike. As examples, the set of exclusion factors may include user age,past medical history, medical conditions, psychological state, userpreference, or the like (e.g., user age diminishes the advisability of atreatment option of “surgery” for a particular user). In embodiments,detecting the set of exclusion factors may include parsing a treatmentoption management database to identify and extract the set of exclusionfactors used to determine the rank-order for the set of treatmentoptions. Other methods of detecting the set of exclusion factors todetermine the relationship between the first preference score and thefirst ranking for the first treatment option are also possible.

At block 440, a set of rationale data may be generated. The set ofrationale data may be generated for the first treatment option withrespect to the first ranking based on the relationship between the firstpreference score and the first ranking. Generally, generating caninclude formulating, creating, instantiating, producing, assembling,structuring, arranging, organizing, or otherwise establishing the set ofrationale data for the first treatment option with respect to the firstranking based on the relationship between the first preference score andthe first ranking. The set of rationale data may include a collection ofinformation that indicates an explanation, reason, motivation, orjustification for why one or more treatment options of the set oftreatment options are ranked as they are in the rank-order. Inembodiments, the set of rationale data may include a portion of the setof attributes, the set of exclusion factors, or both. In embodiments,generating the set of rationale data may include aggregating one or moreof a subset of the set of attributes or a subset of the set of exclusionfactors based on the relationship between the first preference score andthe first ranking. As an example, consider a first treatment option ofpneumonectomy for a lung cancer patient. The first treatment option ofpneumonectomy may be associated with a first preference score of 8(e.g., recommended), and be assigned a first ranking of “28th of 30treatment options” in the rank-order. Accordingly, based on therelationship between the first preference score and the first ranking, asubset of the set of attributes and a subset of the set of exclusionfactors for the first treatment option may be aggregated to generate aset of rationale data of “Pneumonectomy is often a recommended treatmentfor this patient due to attributes of: Efficient removal of affectedtissue and prevention of malignant cell spreading; however, due to anexclusion factor for the patient of severe chronic airways disease,other treatments are recommended.” As such, the set of rationale datamay utilize the set of attributes and the set of exclusion factors toprovide a justification/reasoning for why the first treatment option wasranked as it was (e.g., and explain the discrepancy between the firstpreference score and the first rank). Other methods of generating theset of rationale data for the first treatment option with respect to thefirst ranking based on the relationship between the first preferencescore and the first ranking are also possible.

At block 450, the set of rationale data may be configured for a user.The set of rationale data may be configured for the user based on a userprofile for the user. Generally, configuring can include formulating,arranging, modifying, organizing, adapting, personalizing, orcustomizing the set of rationale data for the user based on a userprofile for the user. The user profile may include a collection ofinformation or data associated with a specific user (e.g., individual,patient). The user profile may include information regarding the medicalhistory, medical conditions (e.g., comorbidities), prescriptions, age,personality, preferences, and other data for the user. In embodiments,configuring the set of rationale data may include customizing therationale text based on the user profile. In certain embodiments,configuring may include removing one or more attributes or exclusionfactors from the set of rationale data that are not relevant to the user(e.g., an attribute describing the efficacy of a treatment option ontumor treatment may not be relevant to a user who does not have atumor). In certain embodiments, configuring may include parsing the userprofile data for the user to identify one or more elements that arerelevant to the set of treatment options, and appending additional setsof rationale data with respect to the identified elements. As anexample, for a user with a history of knee pain (e.g., as indicated bythe user profile for the user), a set of rationale data for a firsttreatment option indicating an exercise regimen may be modified toappend an additional set of rationale data that describes how theexercise regimen was structured to include activities associated withlow-impact to the knees of the user. As such, the set of rationale datamay be customized for the user based on the user profile. Other methodsof configuring the set of rationale data for the user based on a userprofile for the user are also possible.

At block 460, the set of rationale data which is configured for the usermay be provided. Generally, providing can include conveying, sending,relaying, supplying, transmitting, delivering, or otherwise presentingthe set of rationale data which is configured for the user. Inembodiments, providing can include presenting the set of rationale datatogether with the rank-order for the set of treatment options such thateach treatment option of the set of treatment options is associated withan explanation for the attributes and exclusion factors that resulted inits ranking. In certain embodiments, providing may include displayingthe set of rationale data in a graphical user interface to be viewed bya user. Consider the following example. A set of treatment options ofbrachytherapy, proton therapy, stereotactic body radiation therapy andtomotherapy may be associated with a rank-order that defines a hierarchyof tomotherapy (e.g., rank 1) followed by proton therapy (e.g., rank 2),brachytherapy (e.g., rank 3), and stereotactic body radiation therapy(e.g., rank 4). As described herein, a set of rationale data may begenerated for the set of treatment options that provides a set ofattributes and a set of exclusion factors justifying the rank of eachtreatment option may be generated for the set of treatment options.Accordingly, providing may include structuring a visual interface layoutthat allows a user to view each treatment option and associated ranking,together with corresponding rationale data that explains the reasoningfor the hierarchy of the set of treatment options. Other methods ofproviding the set of rationale data which is configured for the user arealso possible.

Method 400 concludes at block 499. As described herein, aspects ofmethod 400 relate to generating rationales for a set of treatmentoptions. Aspects of method 400 may provide performance or efficiencybenefits related to rationale generation management. As an example,providing a set of rationale data for a set of treatment options maypromote treatment option reliability and facilitate treatment optionselection (e.g., users may be informed of why particular treatmentoptions are ranked as they are). Leveraging rationale data with respectto a set of treatment options may be associated with benefits such astreatment option ranking validity, accuracy, and rationale generationefficiency. Aspects may save resources such as bandwidth, processing, ormemory.

FIG. 5 shows an example system 500 for generating rationales fortreatment options, according to embodiments. The example system 500 mayinclude a processor 506 and a memory 508 to facilitate implementation ofgenerating rationales for treatment options. The example system 500 mayinclude a database 502 (e.g., rationale data database). In embodiments,the example system 500 may include a rationale data generationmanagement system 505. The rationale data generation management system505 may be communicatively connected to the database 502, and beconfigured to receive data 504 related to rationale data generation(e.g., treatment options, rank orders, preference scores, userprofiles). The rationale data generation management system 505 mayinclude a receiving module 510 to receive a set of performance scores, areceiving module 515 to receive a rank-order, a determining module 520to determine a relationship between a first preference score and a firstranking for a first treatment option, a generating module 525 togenerate a set of rationale data, a configuring module 530 to configurethe set of rationale data for a user, and a providing module 535 toprovide the rationale data. The rationale data generation managementsystem 505 may be communicatively connected with a module managementsystem 550 that includes one or more modules for implementing aspects ofgenerating rationales for a set of treatment options.

In embodiments, it may be determined that the first preference scoreachieves a match with respect to the first ranking at module 552. Thematch may be determined based on the relationship between the firstpreference score and the first ranking. Generally, determining caninclude computing, formulating, detecting, extracting, calculating,identifying, or otherwise ascertaining the match between the firstpreference score and the first ranking. The match may include asimilarity, congruence, correlation, or correspondence between the firstpreference score and the first ranking. As an example, the match mayindicate that both the first preference score and the first rankingcategorize the first treatment option as belonging to the samerecommendation category (e.g., recommended, for consideration, notrecommended). In embodiments, determining the match may includeanalyzing the first preference score with respect to the first rankingto compute a correlation score (e.g., indication of the degree/extent ofsimilarity), and subsequently ascertaining that the correlation scorefor the first preference score and the first ranking achieve adesignated similarity threshold. As an example, a first treatment optionmay be associated with a first preference score of 3 and a ranking of19th out of 20 treatment options. Accordingly, a correlation score of“98%” (e.g., the degree of similarity between the first preference scoreand the first treatment option) may be computed and compared to adesignated similarity threshold of “90%.” Accordingly, as thecorrelation score of 98% achieves the designated similarity threshold of90%, it may be determined that the first preference score achieves amatch with respect to the first ranking. Other methods of determiningthat the first preference score achieves a match with respect to thefirst ranking are also possible.

In embodiments, a first subset of attributes of the set of attributes ofthe set of attributes may be compiled to generate the set of rationaledata in an automated fashion. The first subset of attributes of the setof attributes may correspond to the first treatment option of the set oftreatment options. Generally, compiling can include collecting,assembling, accumulating, gathering, or otherwise aggregating the firstsubset of attributes of the set of attributes to generate the set ofrationale data. The first subset of attributes may include a portion ofthe set of attributes that correspond to (e.g., relate to, are relevantto, associated with) the first treatment option of the set of treatmentoptions. In embodiments, compiling may include summarizing the subset ofattributes that led to generation of the first preference score for thefirst treatment option (e.g., no exclusion factors significantlyaffected the score, and therefore do not need to be included in therationale). As an example, consider a first treatment option of “dosedense AC (doxorubicin/cyclophosphamide)” that is associated with a firstpreference score of 10 and a first ranking of 1. In response todetermining a match between the first preference score and the firstranking, a set of rationale text may be compiled that indicates “dosedense AC (doxorubicin/cyclophosphamide) is a recommended treatment forpatients who are HER2 negative, have positive nodes, and a large tumor.”Other methods of compiling the first subset of attributes of the set ofattributes to generate the set of rationale data in an automated fashionare also possible.

In embodiments, it may be determined that the first preference scorefails to achieve a match with respect to the first ranking at module554. The failure to achieve the match may be determined based on therelationship between the first preference score and the first ranking.Generally, determining can include computing, formulating, detecting,extracting, calculating, identifying, or otherwise ascertaining that thefirst preference score fails to achieve the match with respect to thefirst ranking. In embodiments, failing to achieve the match may includedetecting a mismatch, dissimilarity, incongruence, divergence, ordisparity between the first preference score and the first ranking. Asan example, the mismatch may indicate that the first preference scoreand the first ranking categorize the first treatment option intodifferent recommendation categories (e.g., the first preference scorerecommends the first treatment option, while the first ranking does notrecommend the first treatment option). In embodiments, determining themismatch may include analyzing the first preference score with respectto the first ranking to compute a correlation score, and subsequentlyascertaining that the correlation score for the first preference scoreand the first ranking does not achieve a designated similaritythreshold. As an example, a first treatment option may be associatedwith a first preference score of 7 (e.g., indicating that the firsttreatment option is recommended) and a first ranking of 10th out of 10treatment options (e.g., indicating that the first treatment option isnot recommended). Accordingly, a correlation score of 44% may becomputed and compared to a designated similarity threshold of 90%, andit may be determined that he first preference score fails to achieve amatch with respect to the first ranking. Other methods of determiningthat the first preference score fails to achieve the match with respectto the first ranking are also possible.

In embodiments, both a first subset of attributes of the set ofattributes and a first subset of exclusion factors of the set ofexclusion factors may be compiled to generate the set of rationale datain an automated fashion. The first subset of attributes of the set ofattributes and the first subset of exclusion factors of the set ofexclusion factors may correspond to the first treatment option of theset of treatment options. Generally, compiling can include collecting,assembling, accumulating, gathering, or otherwise aggregating the firstsubset of attributes of the set of attributes and the first subset ofexclusion factors of the set of exclusion factors to generate the set ofrationale data. As described herein, the first subset of attributes andthe first subset of exclusion factors may include portions of the set ofattributes and the set of exclusion factors, respectively, thatcorrespond to (e.g., relate to, are relevant to, associated with) thefirst treatment option of the set of treatment options. In embodiments,in the event that that the first preference score mismatches the firstranking (e.g., a treatment option has a high or medium preference score,but the first ranking places it in a lower category), compiling mayinclude summarizing the subset of attributes as well as the subset ofexclusion factors that resulted in the first ranking for the firsttreatment option (e.g., attributes and exclusion factors thatimpacted/contributed to the ranking). As an example, consider that afirst treatment option of “dose dense AC (doxorubicin/cyclophosphamide)”is associated with a first preference score of 10 and a first ranking of7th out of 10 treatment options (e.g., e.g., the first preference scoreand the first ranking mismatch one another). Accordingly, in response todetermining the mismatch between the first preference score and thefirst ranking, a set of rationale data may be compiled that indicates“dose dense AC (doxorubicin/cyclophosphamide) is often a recommendedtreatment for patients who are HER2 negative, have positive nodes, and alarge tumor; however, due to the patient's grade 3 peripheralneuropathy, this treatment should only be used with caution. There aremore preferred treatments available.” Other methods of compiling thefirst subset of attributes of the set of attributes and the first subsetof exclusion factors of the set of exclusion factors to generate the setof rationale data in an automated fashion are also possible.

In embodiments, it may be determined that the first preference scoreachieves a discrepancy threshold with respect to the first ranking atmodule 556. Achieving of the discrepancy threshold may be determinedbased on the relationship between the first preference score and thefirst ranking. Generally, determining can include computing,formulating, detecting, extracting, calculating, identifying, orotherwise ascertaining that the first preference score fails achievesthe discrepancy threshold with respect to the first ranking. Thediscrepancy threshold may include a benchmark, criterion, or referencevalue that defines a boundary with respect to the correlation betweenthe first preference score and the first ranking. For instance, thediscrepancy threshold may include a designated degree of similarity(e.g., dissimilarity) between the first preference score and the firstranking, such that those preference scores that achieve the discrepancythreshold are ascertained to be substantially different, divergent, orincongruous with respect to the first ranking. For instance, thediscrepancy threshold may include a designated correlation score of“50%” (e.g., preference scores and rankings having correlation scores ofless than 50% may be considered to achieve the discrepancy threshold).As an example, consider that a first treatment option has a firstpreference score of 2 (e.g., indicating that it is not recommended), butis associated with a first ranking of 3rd of 25 treatment options (e.g.,because other treatment options have been ruled out due to variousfactors). Accordingly, determining may include computing a correlationscore of 32% for the first preference score and the first ranking, andsubsequently comparing it to the designated correlation score of “50%”defined by the discrepancy threshold. Accordingly, it may be ascertainedthat the first preference score achieves the discrepancy threshold withrespect to the first ranking. Other methods of determining that thefirst preference score achieves the discrepancy threshold with respectto the first ranking are also possible.

In embodiments, both a second subset of attributes of the set ofattributes and a second subset of exclusion factors of the set ofexclusion factors that may be compiled to generate the set of rationaledata in an automated fashion. The second subset of attributes of the setof attributes and the second subset of exclusion factors of the set ofexclusion factors may correspond to a second treatment option of the setof treatment options. Generally, compiling can include collecting,assembling, accumulating, gathering, or otherwise aggregating the secondsubset of attributes of the set of attributes and the second subset ofexclusion factors of the set of exclusion factors to generate the set ofrationale data. Aspects of the disclosure relate to the recognitionthat, in some situations, a treatment option may be associated with alow preference score, but still be ranked highly in the rank-order as aresult of other treatment options of the set of treatment options beingdiscarded due to one or more exclusion factors. Accordingly, aspects ofthe disclosure relate to compiling a set of rationale data to indicatethat one or more other treatment options of the set of treatment optionshave been removed from consideration, and that the ranking for the firsttreatment option of the set of treatment options has been elevated inthe rank-order. In embodiments, compiling the set of rationale data mayinclude analyzing the attributes and exclusion factors associated withthe second treatment option to ascertain a second subset of attributesand a second subset of exclusion factors that impacted the ranking ofthe second treatment with respect to the rank-order for the set oftreatment options. As an example, consider that a first treatment optionof “carboplatin” that is associated with a first preferred score of 3(e.g., indicating that it is not recommended), but a rank-order of 2ndout of 10 treatment options (e.g., indicating that it is a recommendedtreatment). Accordingly, a second set of attributes and a second subsetof exclusion factors may be compiled to generate a set of rationale dataindicating that “Carboplatin is typically not a recommended treatmentfor patients who are HER2 negative, have positive nodes, and a largetumor, however the patient's poor creatine clearance has contraindicatedthe use of paclitaxel-containing regiments and the patient's neuropathyhas contraindicated the use of oxaliplatin, therefore carboplatin isrecommended out of the options available to the patient.” Other methodsof compiling both the second subset of attributes of the set ofattributes and the second subset of exclusion factors of the set ofexclusion factors to generate the set of rationale data in an automatedfashion are also possible.

In embodiments, the set of attributes may be filtered at module 558. Theset of attributes may be filtered in an automated fashion based on a setof attribute priority criteria to remove a second subset of attributesfrom the set of attributes. Aspects of the disclosure relate to therecognition that, in some situations, the number of attributesassociated with a particular treatment option may exceed a threshold(e.g., 10 or more attributes), and it may not be desirable to includeall of them in the set of rationale data. Accordingly, aspects of thedisclosure relate to filtering the set of attributes based on a set ofattribute priority criteria to remove a second subset of attributes fromthe set of attributes. Generally, filtering can include removing,separating, refining, arranging, discarding, or otherwise organizing theset of attributes based on the set of attribute priority criteria. Theset of attribute priority criteria may include a collection of rules,benchmarks, guidelines, requirements, stipulations, and other parametersthat define which attributes are considered to be relevant/germane to aparticular treatment option. In embodiments, filtering may includeassigning a priority score to one or more attributes based on the set ofattribute priority criteria. The priority score may be a quantitativeindication of the degree of priority of a particular attribute. Asexamples, filtering may include prioritizing those attributes that areused with respect to both preference score determination and exclusionfactor determination, attributes that are associated with “Required”tags (e.g., as opposed to “Optional”), attributes that impact thepreference score for one or more treatment options (e.g., if changingthe value of an attribute of “HER2Status” from positive to negativewould impact the preference score of a treatment option, the attributemay be prioritized according to the level of impact it would have on thepreference score), or attributes included in training cases orhistorical cohorts for patients. Accordingly, priority scores may beassigned to each attribute of the set of attributes, and the set ofattributes may subsequently be filtered to remove those attributes thatdo not achieve a threshold priority score from the set of rationale data(e.g., remove those attributes with priority scores of less than 75).Other methods of filtering the set of attributes in an automated fashionbased on a set of attribute priority criteria are also possible.

In embodiments, the receiving, the receiving, the determining, thegenerating, the configuring, the providing, and the other stepsdescribed herein may each be executed in a dynamic fashion at module560. The steps described herein may be executed in a dynamic fashion tostreamline rationale data generation for a set of treatment options. Forinstance, the receiving, the receiving, the determining, the generating,the configuring, the providing, and the other steps described herein mayoccur in real-time, ongoing, or on-the-fly. As an example, one or moresteps described herein may be performed on-the-fly (e.g., sets ofrationale data may be dynamically generated, configured based on userprofiles, and provided for users as sets of preference scores andrank-orders for a set of treatment options are received in real-time) inorder to streamline (e.g., facilitate, promote, enhance) rationale datageneration for a set of treatment options. Other methods of performingthe steps described herein are also possible.

In embodiments, the receiving, the receiving, the determining, thegenerating, the configuring, the providing, and the other stepsdescribed herein may each be executed in an automated fashion at block562. The steps described herein may be executed in an automated fashionwithout user intervention. In embodiments, the receiving, the receiving,the determining, the generating, the configuring, the providing, and theother steps described herein may be carried out by an internal rationaledata generation management module maintained in a persistent storagedevice of a local computing device (e.g., network node). In embodiments,the receiving, the receiving, the determining, the generating, theconfiguring, the providing, and the other steps described herein may becarried out by an external rationale data generation management modulehosted by a remote computing device or server (e.g., server accessiblevia a subscription, usage-based, or other service model). In this way,aspects of rationale data generation for a set of treatment options maybe performed using automated computing machinery without manual action.Other methods of performing the steps described herein are alsopossible.

FIG. 6 depicts an example of generating rationales for a set oftreatment options, according to embodiments. In embodiments, a graphicaluser interface 600 may be used to provide a visual representation of aset of treatment options. As described herein, the set of treatmentoptions may be associated with a set of preference scores that provide aquantitative indication of the appropriateness or suitability of one ormore treatment options of the set of treatment options for a user. Inembodiments, the set of preference scores may indicate the suitabilityof a treatment option for a user based on the assumption that the useris not associated with other attributes or factors (e.g., comorbidities)that would affect the treatment (e.g., an ideal patient). Inembodiments, the set of treatment options may be associated with arank-order that defines a hierarchy for the set of treatment options,such that those treatment options located higher within the rank-orderare generally associated with greater reliability, suitability, orpreference (e.g., with respect to a particular user). Other methods ofusing a graphical user interface 600 to illustrate a set of treatmentoptions are also possible.

FIG. 7 depicts an example of generating rationales for a set oftreatment options, according to embodiments. In embodiments, apreference score index 700 may be utilized to determine a recommendationfor a treatment option of the set of treatment options based on a set ofpreference scores for the set of treatment options (e.g., in the eventthat other attributes or exclusion factors do not impact the ranking ofthe set of treatment options). For instance, as shown in the preferencescore index 700, treatment options associated with a preference scorebetween 1 and 3 may be categorized as “not recommended,” treatmentoptions associated with a preference score between 4 and 6 may becategorized as “for consideration,” and treatment options associatedwith a preference score between 7 and 10 may be categorized asrecommended. Other methods of using the set of preference scores tocategorize the set of treatment options are also possible.

FIG. 8 depicts an example of generating rationales for a set oftreatment options, according to embodiments. In embodiments, a rationaledata generation rubric 800 may be used to facilitate generation of theset of rationale data for a set of treatment options. As describedherein, in the event that the preference score for a treatment optionachieves a match with respect to the ranking (e.g., preference score ishigh and treatment is green/recommended, preference score is medium andtreatment is yellow/for consideration, preference score is low andtreatment is red/not recommended) for the treatment specified by therank-order, the set of rationale data may be generated by summarizingthe attributes that contribute to computation of the preference score(e.g., no other factors significantly affected the score and thereforedon't need to be included). In the event that a treatment option isassociated with a preference score that does not match the rankingspecified by the rank-order (e.g., treatment option has a high or mediumpreference score, but the rank-order places it in a lower category suchas red/not recommended), the set of rationale data may be generated bysummarizing the attributes as well as the exclusion factors that haveinfluenced the ranking of the treatment option. In certain embodiments,in the event that a first treatment option is associated with a lowpreference score but is ranked substantially highly in the rank-order(e.g., other treatment options have been discarded/ruled-out based onexclusion factors), the set of rationale data may be generated bysummarizing the attributes and exclusion factors that resulted indiscarding of the other treatment options that were in considerationwith respect to the first treatment operation. Other methods ofgenerating the set of rationale data are also possible.

In addition to embodiments described above, other embodiments havingfewer operational steps, more operational steps, or differentoperational steps are contemplated. Also, some embodiments may performsome or all of the above operational steps in a different order. Inembodiments, operational steps may be performed in response to otheroperational steps. The modules are listed and described illustrativelyaccording to an embodiment and are not meant to indicate necessity of aparticular module or exclusivity of other potential modules (orfunctions/purposes as applied to a specific module).

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

Embodiments of the present disclosure may also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments may include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments may also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement portions of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing for use of the systems.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the variousembodiments. As used herein, the singular forms “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. “Set of,” “group of,” “bunch of,” etc. are intendedto include one or more. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of the stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. In the previous detaileddescription of exemplary embodiments of the various embodiments,reference was made to the accompanying drawings (where like numbersrepresent like elements), which form a part hereof, and in which isshown by way of illustration specific exemplary embodiments in which thevarious embodiments may be practiced. These embodiments were describedin sufficient detail to enable those skilled in the art to practice theembodiments, but other embodiments may be used and logical, mechanical,electrical, and other changes may be made without departing from thescope of the various embodiments. In the previous description, numerousspecific details were set forth to provide a thorough understanding thevarious embodiments. But, the various embodiments may be practicedwithout these specific details. In other instances, well-known circuits,structures, and techniques have not been shown in detail in order not toobscure embodiments.

What is claimed is:
 1. A computer-implemented method for automaticallygenerating rationales for a set of treatment options, the methodcomprising: receiving, with respect to the set of treatment options, aset of preference scores that indicates a first preference score for afirst treatment option of the set of treatment options, whereinreceiving the set of preference scores includes using a treatment optionmanagement engine to compute the set of preference scores for the set oftreatment options based on a historical archive of medical records;receiving, with respect to the set of treatment options, a rank-orderthat indicates a score hierarchy of treatment options containing a firstranking for the first treatment option of the set of treatment options;computing a correlation score based on analyzing the first preferencescore and the first ranking, wherein the set of preference scores areexpressed as integer values between 0 and 10; aggregating one or moresubsets of a set of attributes and one or more subsets of a set ofexclusion factors to generate a set of rationale data for the firsttreatment option with respect to the first ranking, based on therelationship between the first preference score and the first ranking;configuring, based on a user profile for a user, the set of rationaledata for the user; and providing the set of rationale data which isconfigured for the user.
 2. The method of claim 1, further comprising:detecting a set of attributes to determine the relationship between thefirst preference score and the first ranking for the first treatmentoption, wherein the set of attributes define a merit feature of the setof treatment options, and wherein the merit feature comprises any one ormore of a collection of traits, properties, qualities, characteristics,or features of the set of treatment options that contribute to adetermination of the rank-order for the set of treatment options.
 3. Themethod of claim 2, further comprising: detecting a set of exclusionfactors to determine the relationship between the first preference scoreand the first ranking for the first treatment option, wherein the set ofexclusion factors define a demerit feature of the set of treatmentoptions, and wherein the demerit feature comprises any one or more of acollection of traits, properties, qualities, characteristics, orfeatures of the set of treatment options that contribute to adetermination of the rank-order for the set of treatment options.
 4. Themethod of claim 3, further comprising: determining, based on therelationship between the first preference score and the first ranking,that the first preference score achieves a match with respect to thefirst ranking; and compiling, to generate the set of rationale data inan automated fashion, a first subset of attributes of the set ofattributes that corresponds to the first treatment option of the set oftreatment options.
 5. The method of claim 3, further comprising:determining, based on the relationship between the first preferencescore and the first ranking, that the first preference score fails toachieve a match with respect to the first ranking; and compiling, togenerate the set of rationale data in an automated fashion, both a firstsubset of attributes of the set of attributes that corresponds to thefirst treatment option of the set of treatment options and a firstsubset of exclusion factors of the set of exclusion factors thatcorresponds to the first treatment option of the set of treatmentoptions.
 6. The method of claim 3, further comprising: determining,based on the relationship between the first preference score and thefirst ranking, that the first preference score achieves a discrepancythreshold with respect to the first ranking; and compiling, to generatethe set of rationale data in an automated fashion, both a second subsetof attributes of the set of attributes that corresponds to a secondtreatment option of the set of treatment options and a second subset ofexclusion factors of the set of exclusion factors that corresponds tothe second treatment option of the set of treatment options.
 7. Themethod of claim 3, further comprising: filtering, in an automatedfashion based on a set of attribute priority criteria, the set ofattributes to remove a second subset of attributes from the set ofattributes.
 8. The method of claim 1, further comprising: executing, ina dynamic fashion to streamline generating rationales for the set oftreatment options, each of: the receiving, the receiving, thedetermining, the generating, the configuring, and the providing.
 9. Themethod of claim 1, further comprising: executing, in an automatedfashion without user intervention, each of: the receiving, thereceiving, the determining, the generating, the configuring, and theproviding.
 10. A system for automatically generating rationales for aset of treatment options, the system comprising: a memory having a setof computer readable computer instructions, and a processor forexecuting the set of computer readable instructions, the set of computerreadable instructions including: receiving, with respect to the set oftreatment options, a set of preference scores that indicates a firstpreference score for a first treatment option of the set of treatmentoptions, wherein receiving the set of preference scores includes using atreatment option management engine to compute the set of preferencescores for the set of treatment options based on a historical archive ofmedical records; receiving, with respect to the set of treatmentoptions, a rank-order that indicates a score hierarchy of treatmentoptions containing a first ranking for the first treatment option of theset of treatment options; computing a correlation score based onanalyzing the first preference score and the first ranking, wherein theset of preference scores are expressed as integer values between 0 and10; aggregating one or more subsets of a set of attributes and one ormore subsets of a set of exclusion factors to generate a set ofrationale data for the first treatment option with respect to the firstranking, based on the relationship between the first preference scoreand the first ranking; configuring, based on a user profile for a user,the set of rationale data for the user; and providing the set ofrationale data which is configured for the user.
 11. The method of claim10, further comprising: detecting a set of attributes to determine therelationship between the first preference score and the first rankingfor the first treatment option, wherein the set of attributes define amerit feature of the set of treatment options, and wherein the meritfeature comprises any one or more of a collection of traits, properties,qualities, characteristics, or features of the set of treatment optionsthat contribute to a determination of the rank-order for the set oftreatment options.
 12. The method of claim 11, further comprising:detecting a set of exclusion factors to determine the relationshipbetween the first preference score and the first ranking for the firsttreatment option, wherein the set of exclusion factors define a demeritfeature of the set of treatment options, and wherein the demerit featurecomprises any one or more of a collection of traits, properties,qualities, characteristics, or features of the set of treatment optionsthat contribute to a determination of the rank-order for the set oftreatment options.
 13. The system of claim 12, further comprising:determining, based on the relationship between the first preferencescore and the first ranking, that the first preference score achieves amatch with respect to the first ranking; and compiling, to generate theset of rationale data in an automated fashion, a first subset ofattributes of the set of attributes that corresponds to the firsttreatment option of the set of treatment options.
 14. The system ofclaim 12, further comprising: determining, based on the relationshipbetween the first preference score and the first ranking, that the firstpreference score fails to achieve a match with respect to the firstranking; and compiling, to generate the set of rationale data in anautomated fashion, both a first subset of attributes of the set ofattributes that corresponds to the first treatment option of the set oftreatment options and a first subset of exclusion factors of the set ofexclusion factors that corresponds to the first treatment option of theset of treatment options.
 15. The method of claim 12, furthercomprising: determining, based on the relationship between the firstpreference score and the first ranking, that the first preference scoreachieves a discrepancy threshold with respect to the first ranking; andcompiling, to generate the set of rationale data in an automatedfashion, both a second subset of attributes of the set of attributesthat corresponds to a second treatment option of the set of treatmentoptions and a second subset of exclusion factors of the set of exclusionfactors that corresponds to the second treatment option of the set oftreatment options.
 16. The system of claim 12, further comprising:filtering, in an automated fashion based on a set of attribute prioritycriteria, the set of attributes to remove a second subset of attributesfrom the set of attributes.
 17. The system of claim 10, furthercomprising: executing, in a dynamic fashion to streamline generatingrationales for the set of treatment options, each of: the receiving, thereceiving, the determining, the generating, the configuring, and theproviding.
 18. The system of claim 10, further comprising: executing, inan automated fashion without user intervention, each of: the receiving,the receiving, the determining, the generating, the configuring, and theproviding.
 19. A computer program product for automatically generatingrationales for a set of treatment options, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, wherein the computer readable storagemedium is not a transitory signal per se, the program instructionsexecutable by a processor to cause the processor to perform a methodcomprising: receiving, with respect to the set of treatment options, aset of preference scores that indicates a first preference score for afirst treatment option of the set of treatment options, whereinreceiving the set of preference scores includes using a treatment optionmanagement engine to compute the set of preference scores for the set oftreatment options based on a historical archive of medical records;receiving, with respect to the set of treatment options, a rank-orderthat indicates a score hierarchy of treatment options containing a firstranking for the first treatment option of the set of treatment options;computing a correlation score based on analyzing the first preferencescore and the first ranking, wherein the set of preference scores areexpressed as integer values between 0 and 10; aggregating one or moresubsets of a set of attributes and one or more subsets of a set ofexclusion factors to generate a set of rationale data for the firsttreatment option with respect to the first ranking, based on therelationship between the first preference score and the first ranking;configuring, based on a user profile for a user, the set of rationaledata for the user; and providing the set of rationale data which isconfigured for the user.
 20. The computer program product of claim 19,further comprising: detecting, to determine the relationship between thefirst preference score and the first ranking for the first treatmentoption, a set of attributes that characterize a merit feature of the setof treatment options; detecting, to determine the relationship betweenthe first preference score and the first ranking for the first treatmentoption, a set of exclusion factors that characterize a demerit featureof the set of treatment options; determining, based on the relationshipbetween the first preference score and the first ranking, that the firstpreference score achieves a match with respect to the first ranking;compiling, to generate the set of rationale data in an automatedfashion, a first subset of attributes of the set of attributes thatcorresponds to the first treatment option of the set of treatmentoptions; determining, based on the relationship between the firstpreference score and the first ranking, that the first preference scorefails to achieve a match with respect to the first ranking; compiling,to generate the set of rationale data in an automated fashion, both afirst subset of attributes of the set of attributes that corresponds tothe first treatment option of the set of treatment options and a firstsubset of exclusion factors of the set of exclusion factors thatcorresponds to the first treatment option of the set of treatmentoptions; determining, based on the relationship between the firstpreference score and the first ranking, that the first preference scoreachieves a discrepancy threshold with respect to the first ranking;compiling, to generate the set of rationale data in an automatedfashion, both a second subset of attributes of the set of attributesthat corresponds to a second treatment option of the set of treatmentoptions and a second subset of exclusion factors of the set of exclusionfactors that corresponds to the second treatment option of the set oftreatment options; and filtering, in an automated fashion based on a setof attribute priority criteria, the set of attributes to remove a secondsubset of attributes from the set of attributes.